Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment
Author:
Publisher
Brno University of Technology
Subject
Electrical and Electronic Engineering
Link
https://www.radioeng.cz/fulltexts/2020/20_01_0243_0250.pdf
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Softwarization and virtualization of VoIP networks;The Journal of Supercomputing;2022-04-04
2. A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case;2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC);2022-01-08
3. From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey;Applied Sciences;2021-08-31
4. A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application;IEEE Open Journal of the Communications Society;2021
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